2 10 2. Real-world indoor mobility with simulated prosthetic vision 2.1. Introduction Blindness is a common disability that causes impaired daily living functionality and reduces quality of life (Kempen et al., 2012; Stevens et al., 2013). Amongst all daily life activities, mobility and obstacle avoidance are often reported to be the most problematic (van der Geest & Buimer, 2015). For many cases of blindness there currently exists no effective treatment. However, neuroprosthetic implants are a promising technology for restoring some form of vision via electrical neuro-stimulation in the visual pathway (Chen et al., 2020; Fernández et al., 2020; Lewis et al., 2015; 2016; Pezaris & Reid, 2007; Riazi-Esfahani et al., 2014; Roelfsema et al., 2018; Shepherd et al., 2013; Tehovnik & Slocum, 2013; Tehovnik et al., 2009). Using multiple electrodes, such implants can activate a specific arrangement of visual neurons, based on camera input. This neural stimulation elicits a perceived pattern of localized point-like flashes of light, referred to as phosphenes, which can be used to represent the surroundings. The larger the number of implanted electrodes, the more phosphenes can be elicited. In this study we focus on cortical implants, which compared to other types of implants, such as retinal implants, are expected to have a wider range of therapeutic applicability (Fernández et al., 2020), are less amenable to electrical crosstalk (Davis et al., 2012; Wilke et al., 2011), andcan accommodate a larger number of electrodes. For instance, recently, Chen et al. (2020), successfully implanted over a thousand cortical electrodes to achieve artificial visual perception in macaque monkeys. Although the artificially generated prosthetic percept is relatively limited compared to normal vision, it may provide some elementary perception of the surroundings, reenabling daily living functionality. For mobility in particular, various studies have investigated the benefits of visual neuro-prosthetics in a simulated prosthetic vision (SPV) paradigm with sighted participants. Early work byCha et al. (1992b) used a perforated mask over a CRT monitor to create pixelized vision and demonstrated that 625 simulated phosphenes may provide sufficient information for visually guided mobility. More recent studies report that adequate mobility performance could be achieved with as few as 325 (Srivastava et al., 2009) or even just 60 (Dagnelie et al., 2007) phosphenes in a simple environment. Note that a conclusive interpretation of these results is complicated by differences in the used mobility task and the realism of the phosphene simulation. Besides the number of implanted electrodes, another factor that highly influences the usability of prosthetic implants is the choice of image processing protocol that transfers visual input to an appropriate electrode activation pattern. The translation of complex visual input into a phosphene percept (which by definition is limited), requires efficient reduction of information and selection of the mere essential visual features for a given task. This can be achieved with the use of traditional computer vision approaches, such as edge detection (Boyle et al., 2001; Dowling et al., 2004; Guo et al., 2018), but deep neural network models have also gained increasing interest of prosthetic engineers (e.g., Bollen et al., 2019a; Bollen et al., 2019b; de Ruyter van Steveninck et al., 2022a; Han et al., 2021; Lozano et al., 2018a; 2020; Sanchez-Garcia et al., 2020). Various image processing approaches have been proposed for mobility in particular (Barnes et al., 2011; Dagnelie et al., 2007; Dowling et al., 2006; Dowling et al., 2004; Feng & McCarthy, 2013; McCarthy et al., 2013; 2015; Parikh et al., 2013; Srivastava et al., 2009; vanRheede et al., 2010; Vergnieux et al., 2014; 2017; Zapf et al., 2016). A main line of research
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